控制理论(社会学)
非线性系统
最小二乘函数近似
模糊逻辑
粒子群优化
趋同(经济学)
补偿(心理学)
模糊控制系统
数学
数学优化
计算机科学
控制(管理)
人工智能
统计
经济增长
量子力学
物理
经济
估计员
心理学
精神分析
作者
Tiancheng Zong,Junhong Li,Guoping Li
标识
DOI:10.1080/00207721.2022.2135976
摘要
This paper investigates the parameter estimation of errors-in-variables Wiener (EIV-W) nonlinear systems. In such nonlinear systems, both input and output contain interference noises, and some intermediate processes are also interfered by noises. The hierarchical technology is applied to decompose the whole system into two subsystems firstly. For the linear subsystem, in order to obtain unbiased estimates of model parameters, a bias compensation method is introduced. Then, the bias-compensated least squares (BLS) algorithm is proposed. For the nonlinear subsystem, on the basis of particle swarm optimisation (PSO), the fuzzy control technology is added to improve the ability of jumping out of the local optimum. Thus, a bias-compensated least squares and fuzzy PSO based hierarchical (BLS-FPSO-H) method is derived at last. In simulation, a numerical example and a case study about the carbon fibre stretching process are implemented. Results indicate that the BLS-FPSO-H algorithm can effectively identify EIV-W nonlinear systems, the convergence speed and identification accuracy are greatly improved than the basic PSO method and some other PSO variants.
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